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Co-Location Social Networks: Linking the Physical World and Cyberspace

机译:协同定位社交网络:链接物理世界和网络空间

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摘要

Various dedicated web services in the cyberspace, e.g., social networks, e-commerce, and instant communications, play a significant role in people's daily-life. Billions of people around the world access these services through multiple online identifiers (IDs), and interact with each other in both the cyberspace and the physical world. Thanks to the rapid development of wireless and mobile technologies, nowadays these two kinds of interactions are highly relevant with each other. In order to link between the cyberspace and the physical world, we propose a new type of social network, i.e., co-location social network (CLSN). A CLSN contains online IDs describing people's online presence and offline "encountering" events when people come across each other. By analyzing real data collected from a mainstream ISP in China, which contains 32.7 million IDs across the most popular web services, we build a large-scale CLSN, and explore its unique properties from various aspects. The results indicate that the CLSN is quite different from existing online and offline social networks in terms of classic graph metrics. Moreover, we propose a community-based user identification algorithm to find all online IDs belonging to the same physical user. Using some ground-truth data, we demonstrate that our proposed algorithm achieves a high accuracy in user identification. Finally, we perform a user-centric analysis, and we demonstrate the behavioral difference among different types of users.
机译:网络空间中的各种专用Web服务,例如社交网络,电子商务和即时通信,在人们的日常生活中起着重要作用。全球数十亿人通过多个在线标识符(ID)访问这些服务,并在网络空间和物理世界中相互交互。得益于无线和移动技术的飞速发展,如今,这两种交互已经高度相关。为了在网络空间和物理世界之间建立联系,我们提出了一种新型的社交网络,即共置社交网络(CLSN)。 CLSN包含描述人们在线状态的在线ID,以及当人们彼此相遇时的离线“遇到”事件。通过分析从中国主流ISP收集的真实数据,该数据中包含最受欢迎的Web服务中的3270万个ID,我们构建了一个大型CLSN,并从各个方面探讨了其独特的属性。结果表明,就经典图形指标而言,CLSN与现有的在线和离线社交网络有很大不同。此外,我们提出了一种基于社区的用户识别算法,以查找属于同一物理用户的所有在线ID。使用一些真实的数据,我们证明了我们提出的算法在用户识别方面达到了很高的准确性。最后,我们执行以用户为中心的分析,并证明了不同类型用户之间的行为差​​异。

著录项

  • 来源
    《IEEE transactions on mobile computing》 |2019年第5期|1028-1041|共14页
  • 作者单位

    Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China;

    Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China;

    Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China;

    Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Dept Elect Engn, Beijing 100084, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Social networks; graph analysis; cyber-physical systems;

    机译:社交网络;图分析;网络物理系统;

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